{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 什么是数据分析\n",
    "- 是把隐藏在一些看似杂乱无章的数据背后的信息提炼出来，总结出所研究对象的内在规律\n",
    "    - 使得数据的价值最大化\n",
    "        - 分析用户的消费行为\n",
    "            - 制定促销活动的方案\n",
    "            - 制定促销时间和粒度\n",
    "            - 计算用户的活跃度\n",
    "            - 分析产品的回购力度\n",
    "        - 分析广告点击率\n",
    "            - 决定投放时间\n",
    "            - 制定广告定向人群方案\n",
    "            - 决定相关平台的投放\n",
    "        - ......\n",
    "- 数据分析是用适当的方法对收集来的大量数据进行分析，帮助人们做出判断，以便采取适当的行动\n",
    "    - 保险公司从大量赔付申请数据中判断哪些为骗保的可能\n",
    "    - 支付宝通过从大量的用户消费记录和行为自动调整花呗的额度\n",
    "    - 短视频平台通过用户的点击和观看行为数据针对性的给用户推送喜欢的视频"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 为什么学习数据分析\n",
    "- 有岗位的需求\n",
    "    - 数据竞赛平台\n",
    "- 是Python数据科学的基础\n",
    "- 是机器学习课程的基础"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分析实现流程\n",
    "- 提出问题\n",
    "- 准备数据\n",
    "- 分析数据\n",
    "- 获得结论\n",
    "- 成果可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 课程内容介绍\n",
    "- 基础模块使用学习\n",
    "- 项目实现\n",
    "- 金融量化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 开发环境介绍\n",
    "- anaconda\n",
    "    - 官网：https://www.anaconda.com/\n",
    "    - 集成环境：集成好了数据分析和机器学习中所需要的全部环境\n",
    "        - 注意：\n",
    "            - 安装目录不可以有中文和特殊符号\n",
    "- jupyter\n",
    "    - jupyter就是anaconda提供的一个基于浏览器的可视化开发工具\n",
    "- jupyter的基本使用\n",
    "    - 启动：在终端中录入：jupyter notebook的指令，按下回车\n",
    "    - 新建：\n",
    "        - python3：anaconda中的一个源文件\n",
    "        - cell有两种模式：\n",
    "            - code：编写代码\n",
    "            - markdown：编写笔记\n",
    "    - 快捷键：\n",
    "        - 添加cell：a或者b\n",
    "        - 删除：x\n",
    "        - 修改cell的模式：\n",
    "            - m：修改成markdown模式\n",
    "            - y：修改成code模式\n",
    "        - 执行cell：\n",
    "            - shift+enter 执行并跳转到新的\n",
    "            - ctrl+enter  在当个cell下执行不跳转\n",
    "        - tab：自动补全\n",
    "        - 代开帮助文档：shift+tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分析三剑客\n",
    "- numpy\n",
    "- pandas(重点)\n",
    "- matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy模块\n",
    "- NumPy(Numerical Python) 是 Python 语言中做科学计算的基础库。重在于数值计算，也是大部分Python科学计算库的基础，多用于在大型、多维数组上执行的数值运算。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### numpy的创建\n",
    "- 使用np.array()创建\n",
    "- 使用plt创建\n",
    "- 使用np的routines函数创建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 使用array()创建一个一维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr = np.array([1,2,3])\n",
    "arr\n",
    "\n",
    "np.array()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 使用array()创建一个多维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[1,2,3],[4,5,6]])\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 数组和列表的区别是什么？\n",
    "    - 数组中存储的数据元素类型必须是统一类型\n",
    "    - 优先级：\n",
    "        - 字符串 > 浮点型 > 整数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([1,2.2,3])\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 将外部的一张图片读取加载到numpy数组中，然后尝试改变数组元素的数值查看对原始图片的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './1.jpg'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m img_arr \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mimread(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./1.jpg\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;66;03m#返回的数组，数组中装载的就是图片内容\u001b[39;00m\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(img_arr)\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\matplotlib\\pyplot.py:2195\u001b[0m, in \u001b[0;36mimread\u001b[1;34m(fname, format)\u001b[0m\n\u001b[0;32m   2193\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(matplotlib\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimread)\n\u001b[0;32m   2194\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mimread\u001b[39m(fname, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m-> 2195\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m matplotlib\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimread(fname, \u001b[38;5;28mformat\u001b[39m)\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\matplotlib\\image.py:1563\u001b[0m, in \u001b[0;36mimread\u001b[1;34m(fname, format)\u001b[0m\n\u001b[0;32m   1556\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(parse\u001b[38;5;241m.\u001b[39murlparse(fname)\u001b[38;5;241m.\u001b[39mscheme) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   1557\u001b[0m     \u001b[38;5;66;03m# Pillow doesn't handle URLs directly.\u001b[39;00m\n\u001b[0;32m   1558\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1559\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease open the URL for reading and pass the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult to Pillow, e.g. with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1561\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m``np.array(PIL.Image.open(urllib.request.urlopen(url)))``.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1562\u001b[0m         )\n\u001b[1;32m-> 1563\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m img_open(fname) \u001b[38;5;28;01mas\u001b[39;00m image:\n\u001b[0;32m   1564\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (_pil_png_to_float_array(image)\n\u001b[0;32m   1565\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(image, PIL\u001b[38;5;241m.\u001b[39mPngImagePlugin\u001b[38;5;241m.\u001b[39mPngImageFile) \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[0;32m   1566\u001b[0m             pil_to_array(image))\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\PIL\\Image.py:3227\u001b[0m, in \u001b[0;36mopen\u001b[1;34m(fp, mode, formats)\u001b[0m\n\u001b[0;32m   3224\u001b[0m     filename \u001b[38;5;241m=\u001b[39m fp\n\u001b[0;32m   3226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filename:\n\u001b[1;32m-> 3227\u001b[0m     fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   3228\u001b[0m     exclusive_fp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m   3230\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './1.jpg'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#img_arr = plt.imread('./1.jpg')#返回的数组，数组中装载的就是图片内容\n",
    "#plt.imshow(img_arr)#将numpy数组进行可视化展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid shape (3,) for image data",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[7], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#img_arr = img_arr - 100  #将每一个数组元素都减去100\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(arr)\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\matplotlib\\pyplot.py:2695\u001b[0m, in \u001b[0;36mimshow\u001b[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[0;32m   2689\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mimshow)\n\u001b[0;32m   2690\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mimshow\u001b[39m(\n\u001b[0;32m   2691\u001b[0m         X, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m, aspect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, interpolation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   2692\u001b[0m         alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, vmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, vmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, extent\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   2693\u001b[0m         interpolation_stage\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, filternorm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, filterrad\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4.0\u001b[39m,\n\u001b[0;32m   2694\u001b[0m         resample\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, url\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m-> 2695\u001b[0m     __ret \u001b[38;5;241m=\u001b[39m gca()\u001b[38;5;241m.\u001b[39mimshow(\n\u001b[0;32m   2696\u001b[0m         X, cmap\u001b[38;5;241m=\u001b[39mcmap, norm\u001b[38;5;241m=\u001b[39mnorm, aspect\u001b[38;5;241m=\u001b[39maspect,\n\u001b[0;32m   2697\u001b[0m         interpolation\u001b[38;5;241m=\u001b[39minterpolation, alpha\u001b[38;5;241m=\u001b[39malpha, vmin\u001b[38;5;241m=\u001b[39mvmin,\n\u001b[0;32m   2698\u001b[0m         vmax\u001b[38;5;241m=\u001b[39mvmax, origin\u001b[38;5;241m=\u001b[39morigin, extent\u001b[38;5;241m=\u001b[39mextent,\n\u001b[0;32m   2699\u001b[0m         interpolation_stage\u001b[38;5;241m=\u001b[39minterpolation_stage,\n\u001b[0;32m   2700\u001b[0m         filternorm\u001b[38;5;241m=\u001b[39mfilternorm, filterrad\u001b[38;5;241m=\u001b[39mfilterrad, resample\u001b[38;5;241m=\u001b[39mresample,\n\u001b[0;32m   2701\u001b[0m         url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m: data} \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {}),\n\u001b[0;32m   2702\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   2703\u001b[0m     sci(__ret)\n\u001b[0;32m   2704\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\matplotlib\\__init__.py:1442\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1439\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1440\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1441\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1442\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1444\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1445\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1446\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:5665\u001b[0m, in \u001b[0;36mAxes.imshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[0;32m   5657\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_aspect(aspect)\n\u001b[0;32m   5658\u001b[0m im \u001b[38;5;241m=\u001b[39m mimage\u001b[38;5;241m.\u001b[39mAxesImage(\u001b[38;5;28mself\u001b[39m, cmap\u001b[38;5;241m=\u001b[39mcmap, norm\u001b[38;5;241m=\u001b[39mnorm,\n\u001b[0;32m   5659\u001b[0m                       interpolation\u001b[38;5;241m=\u001b[39minterpolation, origin\u001b[38;5;241m=\u001b[39morigin,\n\u001b[0;32m   5660\u001b[0m                       extent\u001b[38;5;241m=\u001b[39mextent, filternorm\u001b[38;5;241m=\u001b[39mfilternorm,\n\u001b[0;32m   5661\u001b[0m                       filterrad\u001b[38;5;241m=\u001b[39mfilterrad, resample\u001b[38;5;241m=\u001b[39mresample,\n\u001b[0;32m   5662\u001b[0m                       interpolation_stage\u001b[38;5;241m=\u001b[39minterpolation_stage,\n\u001b[0;32m   5663\u001b[0m                       \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m-> 5665\u001b[0m im\u001b[38;5;241m.\u001b[39mset_data(X)\n\u001b[0;32m   5666\u001b[0m im\u001b[38;5;241m.\u001b[39mset_alpha(alpha)\n\u001b[0;32m   5667\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m im\u001b[38;5;241m.\u001b[39mget_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5668\u001b[0m     \u001b[38;5;66;03m# image does not already have clipping set, clip to axes patch\u001b[39;00m\n",
      "File \u001b[1;32mD:\\anaconda\\Lib\\site-packages\\matplotlib\\image.py:710\u001b[0m, in \u001b[0;36m_ImageBase.set_data\u001b[1;34m(self, A)\u001b[0m\n\u001b[0;32m    706\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A[:, :, \u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m    708\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m    709\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m]):\n\u001b[1;32m--> 710\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid shape \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m for image data\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    711\u001b[0m                     \u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A\u001b[38;5;241m.\u001b[39mshape))\n\u001b[0;32m    713\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[0;32m    714\u001b[0m     \u001b[38;5;66;03m# If the input data has values outside the valid range (after\u001b[39;00m\n\u001b[0;32m    715\u001b[0m     \u001b[38;5;66;03m# normalisation), we issue a warning and then clip X to the bounds\u001b[39;00m\n\u001b[0;32m    716\u001b[0m     \u001b[38;5;66;03m# - otherwise casting wraps extreme values, hiding outliers and\u001b[39;00m\n\u001b[0;32m    717\u001b[0m     \u001b[38;5;66;03m# making reliable interpretation impossible.\u001b[39;00m\n\u001b[0;32m    718\u001b[0m     high \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m255\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A\u001b[38;5;241m.\u001b[39mdtype, np\u001b[38;5;241m.\u001b[39minteger) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n",
      "\u001b[1;31mTypeError\u001b[0m: Invalid shape (3,) for image data"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#img_arr = img_arr - 100  #将每一个数组元素都减去100\n",
    "plt.imshow(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- zeros()\n",
    "- ones()\n",
    "- linespace()\n",
    "- arange()\n",
    "- random系列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.zeros(shape=(3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "np.linspace(0,100,num=20) #一维的等差数列数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "np.arange(10,50,step=2) #一维等差数列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.randint(0,100,size=(5,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### numpy的常用属性\n",
    "- shape\n",
    "- ndim\n",
    "- size\n",
    "- dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "arr = np.random.randint(0,100,size=(5,6))\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "arr.shape #返回的是数组的形状"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.ndim #返回的是数组的维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.size #返回数组元素的个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.dtype #返回的是数组元素的类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(arr) #数组的数据类型"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### numpy的数据类型\n",
    "- array(dtype=?):可以设定数据类型\n",
    "- arr.dtype = '?':可以修改数据类型\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([1,2,3])\n",
    "arr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#创建一个数组，指定数组元素类型为int32\n",
    "arr = np.array([1,2,3],dtype='int32')\n",
    "arr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.dtype = 'uint8' #修改数组的元素类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### numpy的索引和切片操作（重点）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 索引操作和列表同理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "arr = np.random.randint(1,100,size=(5,6))\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr[1]  #取出了numpy数组中的下标为1的行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr[[1,3,4]] #取出多行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 切片操作\n",
    "    - 切出前两列数据\n",
    "    - 切出前两行数据\n",
    "    - 切出前两行的前两列的数据\n",
    "    - 数组数据翻转\n",
    "    - 练习：将一张图片上下左右进行翻转操作\n",
    "    - 练习：将图片进行指定区域的裁剪"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#切出arr数组的前两行的数据\n",
    "arr[0:2] #arr[行切片]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#切出arr数组中的前两列\n",
    "arr[:,0:2] #arr[行切片,列切片]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#切出前两行的前两列的数据\n",
    "arr[0:2,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#将数组的行倒置\n",
    "arr[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#将数组的列倒置\n",
    "arr[:,::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#所有元素倒置\n",
    "arr[::-1,::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#将一张图片进行左右翻转\n",
    "img_arr = plt.imread('./1.jpg')\n",
    "plt.imshow(img_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.imshow(img_arr[:,::-1,:]) #img_arr[行，列，颜色]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#图片上下翻转\n",
    "plt.imshow(img_arr[::-1,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#图片裁剪的功能\n",
    "plt.imshow(img_arr[66:200,78:300,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 变形reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "arr#是一个5行6列的二维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将二维的数组变形成1维\n",
    "arr_1 = arr.reshape((30,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将一维变形成多维\n",
    "arr_1.reshape((6,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 级联操作\n",
    "    - 将多个numpy数组进行横向或者纵向的拼接\n",
    "- axis轴向的理解\n",
    "    - 0:列\n",
    "    - 1:行\n",
    "- 问题：\n",
    "    - 级联的两个数组维度一样，但是行列个数不一样会如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "np.concatenate((arr,arr),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_3 = np.concatenate((img_arr,img_arr,img_arr),axis=0)\n",
    "plt.imshow(arr_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 常用的聚合操作\n",
    "- sum,max,min,mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.max(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 常用的数学函数\n",
    "- NumPy 提供了标准的三角函数：sin()、cos()、tan()\n",
    "- numpy.around(a,decimals) 函数返回指定数字的四舍五入值。\n",
    "    - 参数说明：\n",
    "        - a: 数组\n",
    "        - decimals: 舍入的小数位数。 默认值为0。 如果为负，整数将四舍五入到小数点左侧的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "np.sin(2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.around(3.84,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 常用的统计函数\n",
    "- numpy.amin() 和 numpy.amax()，用于计算数组中的元素沿指定轴的最小、最大值。\n",
    "- numpy.ptp():计算数组中元素最大值与最小值的差（最大值 - 最小值）。\n",
    "- numpy.median() 函数用于计算数组 a 中元素的中位数（中值）\n",
    "- 标准差std():标准差是一组数据平均值分散程度的一种度量。\n",
    "    - 公式：std = sqrt(mean((x - x.mean())**2))\n",
    "    - 如果数组是 [1，2，3，4]，则其平均值为 2.5。 因此，差的平方是 [2.25,0.25,0.25,2.25]，并且其平均值的平方根除以 4，即 sqrt(5/4) ，结果为 1.1180339887498949。\n",
    "- 方差var()：统计中的方差（样本方差）是每个样本值与全体样本值的平均数之差的平方值的平均数，即 mean((x - x.mean())** 2)。换句话说，标准差是方差的平方根。\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr[1].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr[1].var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 矩阵相关\n",
    "- NumPy 中包含了一个矩阵库 numpy.matlib，该模块中的函数返回的是一个矩阵，而不是 ndarray 对象。一个 的矩阵是一个由行（row）列（column）元素排列成的矩形阵列。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- numpy.matlib.identity() 函数返回给定大小的单位矩阵。单位矩阵是个方阵，从左上角到右下角的对角线（称为主对角线）上的元素均为 1，除此以外全都为 0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#eye返回一个标准的单位矩阵\n",
    "np.eye(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 转置矩阵\n",
    "    - .T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.T"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 矩阵相乘\n",
    "    - numpy.dot(a, b, out=None) \n",
    "        - a : ndarray 数组\n",
    "        - b : ndarray 数组\n",
    "    - ![image.png](attachment:image.png)\n",
    "    - 第一个矩阵第一行的每个数字（2和1），各自乘以第二个矩阵第一列对应位置的数字（1和1），然后将乘积相加（ 2 x 1 + 1 x 1），得到结果矩阵左上角的那个值3。也就是说，结果矩阵第m行与第n列交叉位置的那个值，等于第一个矩阵第m行与第二个矩阵第n列，对应位置的每个值的乘积之和。\n",
    "    - 线性代数基于矩阵的推导：\n",
    "        - https://www.cnblogs.com/alantu2018/p/8528299.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = np.array([[2,1],[4,3]])\n",
    "a2 = np.array([[1,2],[1,0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.dot(a1,a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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